Postdoctoral Fellowship: OCE-PRF: Using machine learning to investigate temporal dynamics of methane seep fauna at the Ocean Observatories Initiative (OOI) Regional Cabled Array

博士后奖学金：OCE-PRF：利用机器学习研究海洋观测计划 (OOI) 区域有线阵列中甲烷渗漏动物群的时间动态

• 批准号: 2307504
• 负责人: Katharine Bigham
• 金额: $ 30.85万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2307504&HistoricalAwards=false
• 关键词: Postdoctoral; Fellowship; OCE; PRF; Using

Methane hydrate seeps are unique chemosynthetic marine environments where organisms use chemicals from the seafloor rather than sunlight to create energy. They are found on continental margins worldwide (with more than 1,300 off the coast of Oregon and Washington alone) providing important services such as energy storage in the form of methane hydrates and habitat for commercially important fish stocks, as well as contributing to regional biodiversity and productivity. However, it is still unclear whether these relationships are due to enhanced productivity from chemosynthetic production, increased seafloor habitat complexity, or a combination thereof. Nor is it known what timescales these habitat users operate under: are they random one-off interactions by individuals, or regular, predictable interactions by a broader population? Therefore, it is difficult to quantify and fully describe the services methane seeps provide, which is vital information for successful management of these potentially critical habitats, energy resources, and carbon sinks. This research will explore these questions by using a unique time series of imagery and video collected by the Ocean Observatories Initiative (OOI) Regional Cabled Array over more than a decade at Southern Hydrate Ridge, a methane hydrate seep off the coast of Oregon. The project will also involve the development of machine learning pipelines which will make future work with OOI imagery more accessible for researchers and the public, since current, manual methods of annotating imagery are incredibly time consuming. Additionally, this project will support an early career post-doctoral researcher, give two undergraduate students an opportunity to participate in research, and produce a classroom module - geared to a broad undergraduate student audience - on marine technology and imagery based marine research.Specifically, this project will develop multi-taxon detectors as a part of a machine assisted annotation pipeline to process an estimated 37 TB of imagery and video collected by the OOI Regional Cabled Array. The pipeline developed for this project will contribute to a fast-moving field of marine automated imaging which aims to make the large amounts of imagery collected in the world’s oceans every day, as well as historically collected data, more readily usable for scientific research. Visible animals, bacterial mats, and geologic structures will be annotated in the imagery, quantifying the changes in abundances and diversity of the community at the site over time. This data will then be used to investigate the temporal trajectory of the benthic megafauna community at the methane seep and examine how the community is influenced by environmental conditions. Insights will help us understand daily, monthly, and yearly cycles in these environments, which will create better understanding of baseline temporal dynamics. Overall, this project will contribute knowledge of methane hydrate seep ecologic dynamics, providing vital information for characterization and management of these biodiverse, high-carbon, and energy-rich habitats.This project is co-funded by the Directorate for Geosciences to support AI/ML advancement in the geosciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

甲烷水合物渗漏是一种独特的化学合成海洋环境，生物利用海底的化学物质而不是阳光来产生能量。它们分布在世界各地的大陆边缘（仅俄勒冈州和华盛顿州海岸就有1300多个），提供重要的服务，如以甲烷水合物的形式储存能量，为商业上重要的鱼类提供栖息地，同时为区域生物多样性和生产力做出贡献。然而，目前尚不清楚这些关系是由于化学合成生产的生产力提高，海底栖息地复杂性的增加，还是两者的结合。也不知道这些栖息地使用者在什么时间尺度下运作：它们是个体随机的、一次性的互动，还是更广泛的群体有规律的、可预测的互动？因此，很难量化和全面描述甲烷渗漏所提供的服务，这对于成功管理这些潜在的关键栖息地、能源资源和碳汇至关重要。这项研究将通过使用海洋观测倡议（OOI）区域电缆阵列在俄勒冈州海岸外的甲烷水合物渗漏处南部水合物脊十多年来收集的独特时间序列图像和视频来探索这些问题。该项目还将涉及机器学习管道的开发，这将使未来研究人员和公众更容易使用OOI图像，因为目前手动注释图像的方法非常耗时。此外，该项目将支持一名早期职业博士后研究员，为两名本科生提供参与研究的机会，并制作一个面向广大本科生的课堂模块，以海洋技术和基于海洋研究的图像为基础。具体来说，该项目将开发多分类单元检测器，作为机器辅助注释管道的一部分，以处理OOI区域有线阵列收集的约37 TB的图像和视频。为该项目开发的管道将有助于快速发展的海洋自动成像领域，其目的是使每天在世界海洋中收集的大量图像以及历史收集的数据更容易用于科学研究。图像中将标注可见动物、细菌垫和地质结构，量化该地点的群落丰度和多样性随时间的变化。然后，这些数据将用于调查甲烷渗漏处底栖巨型动物群落的时间轨迹，并研究该群落如何受到环境条件的影响。洞察将帮助我们理解这些环境中的每日、每月和每年的周期，这将更好地理解基线时间动态。总体而言，该项目将有助于了解甲烷水合物渗漏生态动态，为这些生物多样性、高碳和能源丰富的栖息地的特征和管理提供重要信息。该项目由地球科学理事会共同资助，旨在支持人工智能/机器学习在地球科学领域的发展。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。